Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
Autor: | Konrad Schindler, Nico Lang, Ralph Dubayah, Nikolai Kalischek, Jan Dirk Wegner, John Armston |
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Přispěvatelé: | University of Zurich, Lang, Nico |
Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning LiDAR Canopy height Mean squared error 530 Physics Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Soil Science Deep ensembles Convolutional neural network Machine Learning (cs.LG) Bayesian deep learning GEDI Uncertainty CNN Range (statistics) Computers in Earth Sciences 1111 Soil Science 1907 Geology Remote sensing business.industry Deep learning 1903 Computers in Earth Sciences Probabilistic logic Geology Atmospheric noise Regression Lidar 10231 Institute for Computational Science Artificial intelligence business |
Zdroj: | Remote Sensing of Environment, 268 |
ISSN: | 0034-4257 |
DOI: | 10.48550/arxiv.2103.03975 |
Popis: | NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Remote Sensing of Environment, 268 ISSN:0034-4257 |
Databáze: | OpenAIRE |
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